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#import libraries
library(plyr)
library(tidyverse)
library(dplyr)
library(corrplot)
library(ggplot2)
library(ggcorrplot)
library(data.table)
data.table 1.12.2 using 2 threads (see ?getDTthreads).  Latest news: r-datatable.com

Attaching package: ‘data.table’

The following objects are masked from ‘package:dplyr’:

    between, first, last

The following object is masked from ‘package:purrr’:

    transpose
#Import dataset diemthi2019.csv
diemthi2019 <- read.csv('~/Desktop/2019 vietnam national high school exam/diemthi2019.csv', row.names = NULL)
#Choose necessary columns and reorder them
head(diemthi2019)
str(diemthi2019)
'data.frame':   811851 obs. of  12 variables:
 $ X               : int  0 1 2 3 4 5 6 7 8 9 ...
 $ Dia             : num  7.75 NA 4.25 4.75 8.25 NA NA 6.25 NA 7.5 ...
 $ GDCD            : num  8.75 NA 5.75 5 7.5 NA NA 8.75 NA 8.5 ...
 $ Hoa             : num  NA 4.5 NA NA NA 5.75 NA NA 3.5 NA ...
 $ Li              : num  NA 8.25 NA NA NA 7.5 NA NA 5.5 NA ...
 $ Ma_mon_ngoai_ngu: Factor w/ 7 levels "","N1","N2","N3",..: 2 2 1 1 2 2 1 2 2 2 ...
 $ Ngoai_ngu       : num  7.6 8 NA NA 4.4 4.4 NA 8.8 2.2 5.2 ...
 $ Sinh            : num  NA 6 NA NA NA 3 NA NA 3.75 NA ...
 $ Su              : num  8.25 NA 4 2.25 7.75 NA 3.25 5.25 NA 3.25 ...
 $ Toan            : num  8.2 8.6 5.2 4.4 5.8 7.2 2.2 5 5.6 6.6 ...
 $ Van             : num  8 6.17 4.75 4.5 6 6.75 5.75 6.5 4.5 7 ...
 $ sbd             : int  24008611 51000032 51000005 51000021 51000013 51000003 51000001 51000011 51000007 51000010 ...
#Import and modify dataset province_code.csv
province_code <- read.csv('~/Desktop/2019 vietnam national high school exam/province_code.csv', sep = ";", row.names = NULL)
#Change column type of province_code.csv
province_code$Province_code <- as.numeric(province_code$Province_code)
province_code$Province_name <- as.factor(province_code$Province_name)
head(province_code)
str(province_code)
'data.frame':   64 obs. of  2 variables:
 $ Province_code: num  1 2 3 4 5 6 7 8 9 10 ...
 $ Province_name: Factor w/ 63 levels "","An Giang",..: 24 29 27 16 22 15 35 38 60 37 ...
#Change column name for diemthi2019.csv
diemthi2019 <- plyr::rename(diemthi2019, c(
    'sbd' = 'Student_index',
    'Toan'= 'Maths',
    'Van' = 'Literature',
    'Ngoai_ngu' = 'Foreign_language',
    'Ma_mon_ngoai_ngu' = 'Foreign_language_code',
    'Li' = 'Physics',
    'Hoa' = 'Chemistry',
    'Sinh' = 'Biology',
    'Su' = 'History',
    'Dia' = 'Geography',
    'GDCD' = 'Citizenship_education'
    )
  )
head(diemthi2019)
#Insert province_code for diemthi2019.csv
Province_code <- ifelse(diemthi2019$Student_index > 10000000,
            substr(as.character(diemthi2019$Student_index), start = 1, stop = 2),
            substr(as.character(diemthi2019$Student_index), start = 1, stop = 1)
)
diemthi2019$Province_code <- as.numeric(Province_code)
#insert foreign language names with respect to code
diemthi2019$Foreign_language_name <- diemthi2019$Foreign_language_code
diemthi2019$Foreign_language_name <- mapvalues(diemthi2019$Foreign_language_code, from=c("N1", "N2", "N3", "N4", "N5", "N6"), to=c("English", "Russian", "French", "Chinese", "German", "Japanese"))
head(diemthi2019)
#Merge diemthi2019.csv and province_code.csv
diemthi2019 <- join(diemthi2019, province_code, by = "Province_code")
head(diemthi2019)
#Calculate scores of combinations A00, A01, B00, C00, D00
diemthi2019$A00 <- diemthi2019$Maths + diemthi2019$Physics + diemthi2019$Chemistry
if(diemthi2019$Foreign_language_code == 'N1') {
  diemthi2019$A01 <- diemthi2019$Maths + diemthi2019$Physics + diemthi2019$Foreign_language
}
the condition has length > 1 and only the first element will be used
diemthi2019$B00 <- diemthi2019$Maths + diemthi2019$Chemistry + diemthi2019$Biology
diemthi2019$C00 <- diemthi2019$Literature + diemthi2019$History + diemthi2019$Geography
if(diemthi2019$Foreign_language_code == 'N1') {
  diemthi2019$D00 <- diemthi2019$Maths + diemthi2019$Literature + diemthi2019$Foreign_language
}
the condition has length > 1 and only the first element will be used
head(diemthi2019)
#Calculate average of natural science combination and social science combination
diemthi2019$Avg_natural_sciences <- (diemthi2019$Physics + diemthi2019$Chemistry + diemthi2019$Biology)/3
diemthi2019$Avg_social_sciences <- (diemthi2019$History + diemthi2019$Geography + diemthi2019$Citizenship_education)/3
#Reorder and choose necessary columns
diemthi2019 <- diemthi2019[c('Student_index', 'Province_code', 'Province_name', 'Maths', 'Literature', 'Foreign_language' ,'Foreign_language_code', 'Foreign_language_name', 'Physics', 'Chemistry', 'Biology', 'Avg_natural_sciences', 'History', 'Geography', 'Citizenship_education', 'Avg_social_sciences', 'A00', 'A01', 'B00', 'C00', 'D00')]
head(diemthi2019)
#numerical df for correlation matrix
diemthi2019_corr <- diemthi2019[c(4:6,9:21)]
#correlation matrix
diemthi2019_corr_matrix <- cor(diemthi2019_corr, use="pairwise.complete.obs")
View(diemthi2019_corr_matrix)
#correlation heatmap
ggcorrplot(diemthi2019_corr_matrix, hc.order = TRUE,
   lab = TRUE)

#Number of students who takes each foreign language test
table(diemthi2019$Foreign_language_name)

          English  Russian   French  Chinese   German Japanese 
   83338   726064      148      646      801       81      773 
#Number of students who took each subject
took_subject <- list()
for(i in c(4:6, 9:11, 13:15)) {
  print(paste('The number of students who took the subject', colnames(diemthi2019[i]), 'is', sum(!is.na(diemthi2019[,i]) == TRUE))) 
}
[1] "The number of students who took the subject Maths is 807762"
[1] "The number of students who took the subject Literature is 799208"
[1] "The number of students who took the subject Foreign_language is 728513"
[1] "The number of students who took the subject Physics is 292166"
[1] "The number of students who took the subject Chemistry is 295544"
[1] "The number of students who took the subject Biology is 291199"
[1] "The number of students who took the subject History is 542729"
[1] "The number of students who took the subject Geography is 535897"
[1] "The number of students who took the subject Citizenship_education is 470905"
#Number of students who are eligible to be considered for admission with each combination A00, A01, B00, C00, D00
combination_eligible <- list()
for(i in c(17:21)) {
  print(paste('The number of students who are eligible to be considered for combination', colnames(diemthi2019[i]), 'is', sum(!is.na(diemthi2019[,i]) == TRUE))) 
  combination_eligible[[i-16]] = table(!is.na(diemthi2019[,i]), dnn = paste(colnames(diemthi2019)[i]))
}
[1] "The number of students who are eligible to be considered for combination A00 is 290759"
[1] "The number of students who are eligible to be considered for combination A01 is 281100"
[1] "The number of students who are eligible to be considered for combination B00 is 291078"
[1] "The number of students who are eligible to be considered for combination C00 is 535427"
[1] "The number of students who are eligible to be considered for combination D00 is 725824"
combination_eligible
[[1]]
A00
 FALSE   TRUE 
521092 290759 

[[2]]
A01
 FALSE   TRUE 
530751 281100 

[[3]]
B00
 FALSE   TRUE 
520773 291078 

[[4]]
C00
 FALSE   TRUE 
276424 535427 

[[5]]
D00
 FALSE   TRUE 
 86027 725824 
#Number of students who took all natural science subjects, all social science subjects, or all of both
print(paste('The number of students who took all natural science subjects is', sum(!is.na(diemthi2019[,13]) == TRUE)))
[1] "The number of students who took all natural science subjects is 542729"
print(paste('The number of students who took all social science subjects is', sum(!is.na(diemthi2019[,17]) == TRUE)))
[1] "The number of students who took all social science subjects is 290759"
print(paste('The number of students who took all natural and social science subjects is', sum(!is.na(diemthi2019[,13]) == TRUE & !is.na(diemthi2019[,17]) == TRUE)))
[1] "The number of students who took all natural and social science subjects is 30746"
#Find the maximum score for each column
for(i in c(4:6,9:21)) {
  print(paste('The maximum score of',colnames(diemthi2019)[i],'is', max(diemthi2019[,i], na.rm = TRUE)))
}
[1] "The maximum score of Maths is 10"
[1] "The maximum score of Literature is 9.5"
[1] "The maximum score of Foreign_language is 10"
[1] "The maximum score of Physics is 10"
[1] "The maximum score of Chemistry is 10"
[1] "The maximum score of Biology is 10"
[1] "The maximum score of Avg_natural_sciences is 9.41666666666667"
[1] "The maximum score of History is 10"
[1] "The maximum score of Geography is 10"
[1] "The maximum score of Citizenship_education is 10"
[1] "The maximum score of Avg_social_sciences is 9.83333333333333"
[1] "The maximum score of A00 is 29.05"
[1] "The maximum score of A01 is 28.9"
[1] "The maximum score of B00 is 29.8"
[1] "The maximum score of C00 is 28.75"
[1] "The maximum score of D00 is 28.4"
histogram_out <- list()
for(i in c(4:6,9:21)) {
  x = diemthi2019[,i]
  histogram_out[[i]] <- ggplot(data.frame(x), aes(x)) + 
    geom_histogram(binwidth = 0.1, color="black", fill="blue") +
    labs(title=paste("Score histogram for",colnames(diemthi2019)[i]), x=colnames(diemthi2019)[i], y="Count") + 
    geom_vline(data = diemthi2019, xintercept = mean(diemthi2019[,i], na.rm = TRUE), color = "red", linetype = "dashed", size = 1)
}
histogram_out
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table_score <- list()
for(i in c(4:6,9:21)) {
  table_score[[i]] = table(diemthi2019[,i], dnn = paste(colnames(diemthi2019)[i],'_scores'))
}

for(i in c(4:6,9:21)) {
  View(table_score[[i]])
}
#table_score
summary(diemthi2019[,3])
province_frequency <- table(diemthi2019[,3])
 #sort(province_frequency, decreasing = FALSE) 
province_frequency <-count(diemthi2019, Province_name)
names(province_frequency) <- c('Province', 'Frequency')
province_frequency <- province_frequency[order(-province_frequency$Frequency),]
par(las=2)
View(province_frequency)
ggplot(data=province_frequency, aes(x=reorder(Province,Frequency),y=Frequency)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  ggtitle(paste('Barplot of number of students by province')) +
  labs(x = "Province", y = "Number of students")

barplot_mean <- list()
for(i in c(4:6,9:21)) {
  x <- aggregate(diemthi2019[,i] ~ diemthi2019$Province_name, diemthi2019, mean)
  names(x) <- c('Province', 'Mean')
  x <- x[order(x$Mean),]
  
  barplot_mean[[i]] <- ggplot(data=x, aes(x=reorder(Province,Mean),y=Mean)) +
  geom_bar(position="dodge",stat="identity") + 
  coord_flip() +
  ggtitle(paste('Barplot for mean of', colnames(diemthi2019)[i], 'by province')) + 
  labs(x = "Province", y = "Mean score")
}
barplot_mean
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---
title: "2019 Vietnam High School National Graduation Exam score analysis"
output: html_notebook
author:
  - name: Hoang Anh NGO
  - affiliation: Ecole Polytechnique, IP Paris
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 


```{r}
#import libraries
library(plyr)
library(tidyverse)
library(dplyr)
library(corrplot)
library(ggplot2)
library(ggcorrplot)
library(data.table)
```


```{r}
#Import dataset diemthi2019.csv
diemthi2019 <- read.csv('~/Desktop/2019 vietnam national high school exam/diemthi2019.csv', row.names = NULL)

#Choose necessary columns and reorder them
head(diemthi2019)
str(diemthi2019)

#Import and modify dataset province_code.csv
province_code <- read.csv('~/Desktop/2019 vietnam national high school exam/province_code.csv', sep = ";", row.names = NULL)

#Change column type of province_code.csv
province_code$Province_code <- as.numeric(province_code$Province_code)
province_code$Province_name <- as.factor(province_code$Province_name)

head(province_code)
str(province_code)
```


```{r}
#Change column name for diemthi2019.csv
diemthi2019 <- plyr::rename(diemthi2019, c(
    'sbd' = 'Student_index',
    'Toan'= 'Maths',
    'Van' = 'Literature',
    'Ngoai_ngu' = 'Foreign_language',
    'Ma_mon_ngoai_ngu' = 'Foreign_language_code',
    'Li' = 'Physics',
    'Hoa' = 'Chemistry',
    'Sinh' = 'Biology',
    'Su' = 'History',
    'Dia' = 'Geography',
    'GDCD' = 'Citizenship_education'
    )
  )
head(diemthi2019)
```

```{r}
#Insert province_code for diemthi2019.csv
Province_code <- ifelse(diemthi2019$Student_index > 10000000,
            substr(as.character(diemthi2019$Student_index), start = 1, stop = 2),
            substr(as.character(diemthi2019$Student_index), start = 1, stop = 1)
)
diemthi2019$Province_code <- as.numeric(Province_code)

#insert foreign language names with respect to code
diemthi2019$Foreign_language_name <- diemthi2019$Foreign_language_code
diemthi2019$Foreign_language_name <- mapvalues(diemthi2019$Foreign_language_code, from=c("N1", "N2", "N3", "N4", "N5", "N6"), to=c("English", "Russian", "French", "Chinese", "German", "Japanese"))

head(diemthi2019)
```

```{r}
#Merge diemthi2019.csv and province_code.csv
diemthi2019 <- join(diemthi2019, province_code, by = "Province_code")
head(diemthi2019)
```


```{r}
#Calculate scores of combinations A00, A01, B00, C00, D00
diemthi2019$A00 <- diemthi2019$Maths + diemthi2019$Physics + diemthi2019$Chemistry
if(diemthi2019$Foreign_language_code == 'N1') {
  diemthi2019$A01 <- diemthi2019$Maths + diemthi2019$Physics + diemthi2019$Foreign_language
}
diemthi2019$B00 <- diemthi2019$Maths + diemthi2019$Chemistry + diemthi2019$Biology
diemthi2019$C00 <- diemthi2019$Literature + diemthi2019$History + diemthi2019$Geography
if(diemthi2019$Foreign_language_code == 'N1') {
  diemthi2019$D00 <- diemthi2019$Maths + diemthi2019$Literature + diemthi2019$Foreign_language
}
head(diemthi2019)

#Calculate average of natural science combination and social science combination
diemthi2019$Avg_natural_sciences <- (diemthi2019$Physics + diemthi2019$Chemistry + diemthi2019$Biology)/3
diemthi2019$Avg_social_sciences <- (diemthi2019$History + diemthi2019$Geography + diemthi2019$Citizenship_education)/3
```

```{r}
#Reorder and choose necessary columns
diemthi2019 <- diemthi2019[c('Student_index', 'Province_code', 'Province_name', 'Maths', 'Literature', 'Foreign_language' ,'Foreign_language_code', 'Foreign_language_name', 'Physics', 'Chemistry', 'Biology', 'Avg_natural_sciences', 'History', 'Geography', 'Citizenship_education', 'Avg_social_sciences', 'A00', 'A01', 'B00', 'C00', 'D00')]
head(diemthi2019)
```


```{r}
#numerical df for correlation matrix
diemthi2019_corr <- diemthi2019[c(4:6,9:21)]

#correlation matrix
diemthi2019_corr_matrix <- cor(diemthi2019_corr, use="pairwise.complete.obs")
View(diemthi2019_corr_matrix)

#correlation heatmap
ggcorrplot(diemthi2019_corr_matrix, hc.order = TRUE,
   lab = TRUE)
```

```{r}
#Number of students who takes each foreign language test
table(diemthi2019$Foreign_language_name)
```

```{r}
#Number of students who took each subject
took_subject <- list()
for(i in c(4:6, 9:11, 13:15)) {
  print(paste('The number of students who took the subject', colnames(diemthi2019[i]), 'is', sum(!is.na(diemthi2019[,i]) == TRUE))) 
}
```

```{r}
#Number of students who are eligible to be considered for admission with each combination A00, A01, B00, C00, D00
combination_eligible <- list()
for(i in c(17:21)) {
  print(paste('The number of students who are eligible to be considered for combination', colnames(diemthi2019[i]), 'is', sum(!is.na(diemthi2019[,i]) == TRUE))) 
  combination_eligible[[i-16]] = table(!is.na(diemthi2019[,i]), dnn = paste(colnames(diemthi2019)[i]))
}
combination_eligible
```

```{r}
#Number of students who took all natural science subjects, all social science subjects, or all of both
print(paste('The number of students who took all natural science subjects is', sum(!is.na(diemthi2019[,13]) == TRUE)))
print(paste('The number of students who took all social science subjects is', sum(!is.na(diemthi2019[,17]) == TRUE)))
print(paste('The number of students who took all natural and social science subjects is', sum(!is.na(diemthi2019[,13]) == TRUE & !is.na(diemthi2019[,17]) == TRUE)))
```


```{r}
#Find the maximum score for each column
for(i in c(4:6,9:21)) {
  print(paste('The maximum score of',colnames(diemthi2019)[i],'is', max(diemthi2019[,i], na.rm = TRUE)))
}
```

```{r}
histogram_out <- list()
for(i in c(4:6,9:21)) {
  x = diemthi2019[,i]
  histogram_out[[i]] <- ggplot(data.frame(x), aes(x)) + 
    geom_histogram(binwidth = 0.1, color="black", fill="blue") +
    labs(title=paste("Score histogram for",colnames(diemthi2019)[i]), x=colnames(diemthi2019)[i], y="Count") + 
    geom_vline(data = diemthi2019, xintercept = mean(diemthi2019[,i], na.rm = TRUE), color = "red", linetype = "dashed", size = 1)
}
histogram_out
```

```{r}
table_score <- list()
for(i in c(4:6,9:21)) {
  table_score[[i]] = table(diemthi2019[,i], dnn = paste(colnames(diemthi2019)[i],'_scores'))
}

for(i in c(4:6,9:21)) {
  View(table_score[[i]])
}
#table_score
```

```{r}
summary(diemthi2019[,3])
```

```{r}
province_frequency <- table(diemthi2019[,3])
 #sort(province_frequency, decreasing = FALSE) 
province_frequency <-count(diemthi2019, Province_name)
names(province_frequency) <- c('Province', 'Frequency')
province_frequency <- province_frequency[order(-province_frequency$Frequency),]
par(las=2)
View(province_frequency)

ggplot(data=province_frequency, aes(x=reorder(Province,Frequency),y=Frequency)) +
  geom_bar(position="dodge", stat="identity") + 
  coord_flip() +
  ggtitle(paste('Barplot of number of students by province')) +
  labs(x = "Province", y = "Number of students")
```


```{r}
barplot_mean <- list()
for(i in c(4:6,9:21)) {
  x <- aggregate(diemthi2019[,i] ~ diemthi2019$Province_name, diemthi2019, mean)
  names(x) <- c('Province', 'Mean')
  x <- x[order(x$Mean),]
  
  barplot_mean[[i]] <- ggplot(data=x, aes(x=reorder(Province,Mean),y=Mean)) +
  geom_bar(position="dodge",stat="identity") + 
  coord_flip() +
  ggtitle(paste('Barplot for mean of', colnames(diemthi2019)[i], 'by province')) + 
  labs(x = "Province", y = "Mean score")
}
barplot_mean
```



